1,720,968 research outputs found
Gestione delle scorte nell'era dell'industria 4.0.
No full textRecentemente l’Industria 4.0 ha rivoluzionato il mondo del lavoro creando sistemi di produzione intelligenti grazie alle sue tecnologie abilitanti. Gli obiettivi principali dell'Industria 4.0 sono il raggiungimento di un più alto livello di efficienza operativa e produttiva, produttività aumentata grazie all’automazione dei processi. Per raggiungere tali obiettivi, le tecnologie chiave abilitanti dell’Industria 4.0 sono: Intelligenza Artificiale (IA), Additive Manufacturing (AM), sistemi IoT, Big Data, reinforcement learning e cloud computing. In questa tesi, partendo dallo studio della letteratura attuale, è stata valutata l'applicazione di queste tecnologie per la gestione di impianti industriali.
In particolare, per quanto riguarda le tecnologie operative, è stato esaminato l’utilizzo di AM per la gestione dei pezzi di ricambio. Questo in quanto la gestione delle parti di ricambio è ostacolata dalla loro domanda intermittente e dagli alti costi di fermo impianto derivanti dall’assenza di una parte di ricambio. Sono state ricavate sperimentalmente le situazioni che favoriscono l’adozione di AM in vari contesti: l’insourcing di AM per la produzione in tempo reale delle parti di ricambio, l’insourcing di AM come strumento per aumentare la resilienza della catena di fornitura di parti di ricambio e AM per la manutenzione preventiva delle parti di ricambio. Sempre riguardo le tecnologie operative è stata esaminato il ruolo di sensori a basso costo per la valutazione del rischio integrato a cui sono soggetti gli operatori in ambito industriale in combinazione con un eliciting indiretto delle categorie di rischio. L'eliciting indiretto è stato confrontato con il più classico eliciting diretto. Confronto che ha evidenziato la superiorità di un eliciting indiretto nel ricostruire le preferenze degli operatori. Questo filone di ricerca è nato dalla necessità di includere gli operatori e le loro caratteristiche univoche nella gestione degli impianti industriali dando importanza al fattore umano che era stato trascurato dall’industria 4.0 e che diventa ora un pilastro della nuova Industria 5.0. Sensori a basso costo per la valutazione del rischio sono stati esaminati anche alla luce del calcolo del rischio ergonomico, sviluppando due diverse applicazioni basate sulla nuova telecamera di profondità Azure Kinect. Infine, alla luce delle tecnologie dell'informazione, sono state testate due diverse tecnologie: l'analisi dei Big Data attraverso algoritmi di clustering è stata sfruttata per facilitare il riordino combinato dei pezzi di ricambio mentre la programmazione dinamica è stata sfruttata per il riordino di articoli in mercati ad alta volatilità.Recently Industry 4.0 was introduced and with the help of its key enabling technologies started to revolutionize human work creating intelligent manufacturing systems. The main goals of Industry 4.0 are to achieve a higher level of operational efficiency and productivity, as well as a higher level of automatization while maximizing productivity. For reaching the said goals the key enabling technologies are Artificial Intelligence (AI), Additive Manufacturing (AM), IoT systems, Big Data, reinforcement learning and cloud computing. In this thesis starting from current literature the application of these technologies for the management of Industrial Plants has been investigated. In particular, regarding operational technologies the use of AM for the management of spare parts has been investigated given the challenges for a correct spare parts management hindered by Recently Industry 4.0 was introduced and with the help of its key enabling technologies started to revolutionize human work creating intelligent manufacturing systems. The main goals of Industry 4.0 are to achieve a higher level of operational efficiency and productivity, as well as a higher level of automatization while maximizing productivity. For reaching the said goals the key enabling technologies are Artificial Intelligence (AI), Additive Manufacturing (AM), IoT systems, Big Data, reinforcement learning and cloud computing. In this thesis starting from current literature the application of these technologies for the management of Industrial Plants has been investigated. In particular, regarding operational technologies the use of AM for the management of spare parts has been investigated given the challenges for a correct spare parts management hindered by their intermittent demand and high cost of shortage. The situations that favor the insourcing of AM have been defined in various contexts: spare parts on demand production, insourced AM as a resilience tool for spare parts supply chain and AM for spare parts preventive maintenance. On the same line of research regarding operational technologies, the role of low-cost sensors for the evaluation of the integrated risk to which operators are subjected has been investigated in combination with an indirect eliciting of risk category weights. Indirect eliciting that has been compared with classical direct eliciting finding the superiority of an indirect eliciting in reconstructing operators’ preferences. This stream of research started from the need to consider humans in the loop that were ignored by Industry 4.0 and thus refer to the newly started Industry 5.0. Low-cost sensors have been investigated also in the light of ergonomic risk developing two different applications based on the new depth camera Azure Kinect to evaluate it semi-automatically. Lastly, in the light of informational technologies two different technologies have been tested: Big Data analytics have been exploited to facilitate the management of spare parts and dynamic programming for the re-order of items in high-volatile markets with demand updates
A PERT model based on the Dampster and Shafer's theory of evidence – application to product development
No full textComparison Semiautomatic NIOSH Lifting Equation: AzKNIOSH versus RGB-based Machine Vision Algorithm
No full textWork related musculoskeletal disorders (WMDs) have a significant impact on industrial productivity and society. With the advent of Industry 5.0, the safety and well-being of human operators are back to being crucial for each modern production system. In this context, many innovative technologies have been developed for ergonomic purposes. Motion Capture (MOCAP) technologies are applied to semi automatically calculate the ergonomic risk in a faster and less expensive way. In the other hand, the usage of MOCAP is not always recommended and data collection with common devices is preferred in industrial environment. For this scope, we compared the effectiveness of a commercial machine vision algorithm (ErgoEdge) based on RGB camera against a developed application based on the depth camera Microsoft Azure Kinect (AzKNIOSH) for NIOSH Lifting Equation computation. Fifty-two tasks in which volunteers performed manual handling of loads were evaluated with both systems, showing a good agreement
Optimization of the logistic “fill rate” key performance indicator through the application of the DMAIC approach
Full text linkMeasuring and monitoring the performances of supply chains over time is a primary interest factor for companies. In this way, it is possible to determine the effectiveness and efficiency of strategies for being competitive in global markets, verify the achievement of the predetermined targets, and establish intervention and improvement measures. In this context, key performance indicators (KPIs) are widely used to measure the numerous activities performed across a supply chain. Numerous KPIs are available in the literature, and they are often customized by each user to make them more suitable for their reference context. This paper analyzes the logistic “fill rate” KPI that characterizes the shipping phase of goods by evaluating the fill rate of the transport unit used. A case study analyzes the fill rate indicator used by a multinational corporation that produces and markets food packaging. Through the DMAIC (Define, Measure, Analyze, Improve, and Control) approach, the criticalities of the current formulation of the index are highlighted, and a new model for calculating the index is proposed and applied experimentally at a plant in northern Italy
Ergonomic Evaluation of an Active Exoskeleton During Multi-Task Manual Lifting: A preliminary Study using AzKCLI.
No full textWork-related musculoskeletal disorders significantly impact industrial productivity and society. With the advent of Industry 5.0, the safety and comfort of human operators are essential for modern production systems. In this context, many innovative technologies have been developed for ergonomic purposes. Exoskeletons are used to support workers and reduce lifting jobs. Motion Capture technologies are applied to evaluate ergonomic risk in an easier, faster and less expensive way. In this paper, we evaluate the risk involved in multi-task manual lifting jobs with and without the assistance of an active exoskeleton through Motion Capture Technology. For this purpose, three different picking routines were performed by five different subjects in a laboratory environment using the Azure Kinect depth camera. Risk assessment was carried out through an Azure Kinect-based tool to automatically calculate the Composite Lifting Index named AzKCLI. Results showed that the usage of the exoskeleton during multi-task manual lifting jobs had a subjective influence on each volunteer’s posture. However, the average risk related to posture did not increase
Pharmaceutical Inventory Management: A Comparative Analysis of Forecasting Techniques and Dynamic Reordering Policies.
No full textThe shortage of products, or stockout, is identified as a critical business issue, leading to disruptions inproduct flow and subsequent economic damage. In particular in the healthcare context, stockouts can pose risks topatients due to the inability to administer essential medications. The study presents an in-depth analysis of a set ofpharmaceutical products based on a five-year database containing information on demand, stock, and orders placed.In particular the aim is to assess the performance of various demand forecasting techniques on this product set andsubsequently find the most cost-effective dynamic reordering policy’s parameters. The efficacy of the forecastingtechniques is selected based on minimizing the Root Mean Square Error (RMSE). Subsequently, a periodic dynamicreview policy is applied to determine the number of orders and resulting backorders, evaluating the total managementcost for the item. This approach allows for the evaluation of the effectiveness of ad hoc forecasting methods for eachproduct compared to using a uniform approach. The results of the analysis provide a detailed overview of theforecasting techniques' performance related to dynamic reordering policy parameters and demonstrate the benefitsearnable with respect to the company classical management
Impact of Industrial Symbiosis on Additive Manufacturing of Spare Parts during Supply Chain Disruptions
Full text linkIn a world increasingly impacted by supply chain disruptions and the demand
for low-emission industrial districts, this study explores how additive manufacturing (AM)
and industrial symbiosis (IS) can transform spare parts supply chains. Through simulation
modelling, conventional and AM-supported SC configurations are compared across scenarios
involving stability, disruptions, and recovery strategies. AM facilitates localised, on-demand
production, improving flexibility and spare parts availability, while IS utilises waste materials
to lower emissions and costs. The findings highlight that integrating AM and IS enhances supply
chain resilience and sustainability, addressing global challenges and advancing circular practices
within industrial ecosystem
Leveraging OpenPose and Kinect: Cutting-Edge technologies for Ergonomic Risk Assessment.
No full textSemiautomatic rapid upper limb assessment methods: validation of AzKRULA
No full textIn the Industry 5.0 era, optimising working posture is crucial to reduce musculoskeletal disorder risks. Rapid upper limb assessment (RULA) is a common evaluation method, but traditional approaches are often subjective, and wearable sensors can be costly and intrusive. Optical sensors offer a more practical alternative for industrial environments. This study compares the effectiveness of an in-house application, AzKRULA based on Microsoft Azure Kinect, with Siemens Jack Tat Suit software for RULA assessment. We evaluated 15 static postures with both AzKRULA and the Jack Tat Suit software, using expert assessments as a reference. The results showed a high level of agreement between AzKRULA, expert evaluations, and the commercial software, highlighting AzKRULA as a cost-effective, rapid tool for ergonomic assessment. Thus, AzKRULA can support ergonomists and health and safety managers in assessing upper-body ergonomic risks in repetitive tasks
Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control
Full text linkDigital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration of smart statistical digital systems into existing manufacturing control systems, exemplified with guidelines to transform an existent statistical process control into a smart statistical process control. Employing the design science research method, the research techniques include a literature review and interviews with experts who critically evaluated the proposed framework. The primary contribution lies in a set of general-purpose guidelines tailored to assist practitioners in manufacturing systems with the implementation of digital, smart technologies aligned with the principles of Industry 4.0. The resulting guidelines specifically target existing manufacturing plants seeking to adopt new technologies to maintain competitiveness. The main implication of the study is that practitioners can utilize the guidelines as a roadmap for the ongoing development and implementation of project management. Furthermore, the study paves the way for open innovation initiatives by breaking down the project into defined steps and encouraging individual or collective open contributions, which consolidates the practice of open innovation in manufacturing systems
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